Research on Elderly Demand Forecasting and Resource Allocation Based on Random Forest Algorithm

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Jie Sun, Yanwei Wang, Tong Yu, Zhengyuan Xu, Chengxiang Bian


As the global population ages, understanding and accurately forecasting the demand for elderly care services becomes increasingly critical for effective resource allocation. This study explores the application of the Random Forest algorithm in forecasting the demand for elderly care services and optimizing resource allocation. The Random Forest algorithm is recognized for its resilience and capacity to manage. large datasets with complex relationships are employed to analyze various factors influencing the demand for elderly care, including demographic trends, health indicators, and socioeconomic variables. Data from past years are utilized to train the model, while future projections are generated to forecast demand under different scenarios. The study aims to provide insights into the evolving needs of the elderly population and support policymakers and healthcare providers in making through the integration of advanced machine learning techniques, this research contributes to enhancing the efficiency and effectiveness of elderly care services, ultimately refining the superiority of life for elderly persons and promoting sustainable healthcare systems.

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